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We present a modified diffusive epidemic process that has a finite threshold on scale-free graphs. The diffusive epidemic process describes the epidemic spreading in a non-sedentary population, and it is a reaction-diffusion process. In the diffusion stage, the individuals can jump between connected nodes, according to different diffusive rates for the infected and susceptible individuals. In the reaction stage, the contagion can happen if there is an infected individual sharing the same node, and infected individuals can spontaneously recover. Our main modification is to turn the number of individuals' interactions independent on the population size by using Gillespie algorithm with a reaction time $t_\mathrm{max}$, exponentially distributed with mean inversely proportional to the node concentration. Our simulation results of the modified model on Barabasi-Albert networks are compatible with a continuous phase transition with a finite threshold from an absorbing phase to an active phase when increasing the concentration. The transition obeys the mean-field critical exponents of the order parameter, its fluctuations and the spatial correlation length, whose values are $β=1$, $γ'=0$ and $ν_\perp=1/2$, respectively. In addition, the system presents logarithmic corrections with pseudo-exponents $\widehatβ=\widehatγ'=-3/2$ on the order parameter and its fluctuations, respectively. The most evident implication of our simulation results is if the individuals avoid social interactions in order to not spread a disease, this leads the system to have a finite threshold in scale-free graphs, allowing for epidemic control.